Assessing fine particulate matter concentrations and trends in southern Ontario, Canada, 2003–2012
Bibliographic record
Abstract
Fine particulate matter is primarily released by transportation, residential and industrial processes. It can cause cardiopulmonary problems and has been attributed to the development of diabetes. Ontario is Canada’s most populous province and shares its southern border with the United States of America. The 2003 Canada-United States Border Air Quality Strategy outlines an initiative to reduce air pollution, specifically targeting southern Ontario due to its proximity to the U.S. and its historical air pollution levels. Ambient air concentrations of fine particulate matter (PM2.5) in southern Ontario were analyzed in this research. The data were obtained from the Ontario Ministry of the Environment. There are 40 stations across Ontario that monitor concentrations of up to six airborne pollutants on an hourly basis. The purpose of this research was to examine ambient air quality trends from 2003 to 2012 by generating prediction surfaces using the ordinary kriging spatial interpolation technique. Average PM2.5 levels for each year as well as maximum pollutant concentrations for the lowest and the highest year were produced. The results showed that fine particulate matter levels decreased, and the maximum levels per year also declined significantly. This indicates that fine particulate matter was greatly reduced and air quality generally improved in terms of PM2.5 during the analysis period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".